<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" dir="ltr" xml:lang="en" lang="en-US"><head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Task Description: Aspect Based Sentiment Analysis (ABSA) &lt; SemEval-2014 Task 4</title>
<meta name="robots" content="index, follow">
<link rel="stylesheet" href="Task%20Description%20%20Aspect%20Based%20Sentiment%20Analysis%20(ABSA)%20(%20SemEval-2014%20Task%204_files/style.css" type="text/css" media="screen">
</head>
<body id="index">
<div id="bhm-main">
	<div class="cleared reset-box"></div>
	<div class="bhm-box bhm-sheet">
		<div class="bhm-box-body bhm-sheet-body">
		<div class="bhm-bar bhm-nav">
		<div class="bhm-nav-outer">
		<ul class="bhm-hmenu">
		<li class="current  index"><a href="http://alt.qcri.org/semeval2014/task4/" title="Task Description: Aspect Based Sentiment Analysis (ABSA)">Home</a></li>
<li class="data-and-tools"><a href="http://alt.qcri.org/semeval2014/task4/index.php?id=data-and-tools" title="Data and Tools">Data and Tools</a></li>
<li class="important-dates"><a href="http://alt.qcri.org/semeval2014/task4/index.php?id=important-dates" title="Important Dates">Important Dates</a></li>
<li class="contact"><a href="http://alt.qcri.org/semeval2014/task4/index.php?id=contact" title="Contact">Contact</a></li>
<li class="google-group"><a href="http://alt.qcri.org/semeval2014/task4/index.php?id=google-group" title="Google Group">Google Group</a></li>
<li class="call-for-participation"><a href="http://alt.qcri.org/semeval2014/task4/index.php?id=call-for-participation" title="Call For Participation">CFP</a></li>
		</ul></div></div>
		<div class="cleared reset-box"></div>
		<div class="bhm-header">
		<div class="bhm-logo">
		<h1 class="bhm-logo-name"><a href="#">SemEval-2014 Task 4</a></h1>
		<h2 class="bhm-logo-text"></h2>
		</div>                
		</div>		
		<div class="cleared reset-box"></div>
		<div class="bhm-layout-wrapper">
			<div class="bhm-content-layout">
			<div class="bhm-content-layout-row">
			<div class="bhm-layout-cell bhm-content">
			<div class="bhm-box bhm-post">
			<div class="bhm-box-body bhm-post-body">
			<div class="bhm-post-inner bhm-article">
			<h2 class="bhm-postheader">Task Description: Aspect Based Sentiment Analysis (ABSA)</h2>
			<div class="bhm-postheadericons bhm-metadata-icons">

			</div>
			<div class="bhm-postcontent">
			<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	Sentiment analysis is increasingly viewed as a vital task both from an 
academic and a commercial standpoint. The majority of current 
approaches, however, attempt to detect the overall polarity of a 
sentence, paragraph, or text span, regardless of the entities mentioned 
(e.g., laptops, restaurants) and their aspects (e.g., battery, screen; 
food, service). By contrast, this task is concerned with aspect based 
sentiment analysis (ABSA), where the goal is to identify the aspects of 
given target entities and the sentiment expressed towards each aspect. 
Datasets consisting of customer reviews with human-authored annotations 
identifying the mentioned aspects of the target entities and the 
sentiment polarity of each aspect will be provided.</p>
<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	In particular, the task consists of the following subtasks:</p>
<p style="text-align: justify; ">
	<br>
	<strong>Subtask 1: <em>Aspect term extraction</em> </strong></p>
<p style="text-align: justify; ">
	Given a set of sentences with pre-identified entities (e.g., 
restaurants), identify the aspect terms present in the sentence and 
return a list containing all the distinct aspect terms. An aspect term 
names a particular aspect of the target entity.</p>
<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	For example, "I liked the <em>service</em> and the <em>staff</em>, but not the <em>food</em>”, “The <em>food</em> was nothing much, but I loved the <em>staff</em>”. Multi-word aspect terms (e.g., “hard disk”) should be treated as single terms (e.g., in “The <em>hard disk</em> is very noisy” the only aspect term is “hard disk”).<br>
	<br>
	<strong> Subtask 2: <em>Aspect term polarity</em></strong></p>
<p style="text-align: justify; ">
	For a given set of aspect terms within a sentence, determine whether 
the polarity of each aspect term is positive, negative, neutral or 
conflict (i.e., both positive and negative).</p>
<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	For example:</p>
<p style="text-align: justify; ">
	“I loved their <strong>fajitas</strong>” → {fajitas: <em>positive</em>}<br>
	“I hated their <strong>fajitas</strong>, but their <strong>salads</strong> were great” → {fajitas: <em>negative</em>, salads: <em>positive</em>}<br>
	“The <strong>fajitas</strong> are their first plate” → {fajitas: <em>neutral</em>}<br>
	“The <strong>fajitas</strong> were great to taste, but not to see” → {fajitas: <em>conflict</em>}<br>
	<br>
	<strong> Subtask 3: <em>Aspect category detection</em></strong></p>
<p style="text-align: justify; ">
	Given a predefined set of aspect categories (e.g., price, food), 
identify the aspect categories discussed in a given sentence. Aspect 
categories are typically coarser than the aspect terms of Subtask 1, and
 they do not necessarily occur as terms in the given sentence.</p>
<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	For example, given the set of aspect categories <strong>{food, service, price, ambience, anecdotes/miscellaneous}</strong>:</p>
<p style="text-align: justify; ">
	“The restaurant was too expensive”&nbsp; → <strong>{price}</strong><br>
	“The restaurant was expensive, but the menu was great” → <strong>{price, food}</strong><br>
	<br>
	<strong> Subtask 4: <em>Aspect category polarity</em></strong></p>
<p style="text-align: justify; ">
	Given a set of pre-identified aspect categories (e.g., <strong>{food, price}</strong>), determine the polarity (<em>positive</em>, <em>negative</em>, <em>neutral</em> or <em>conflict</em>) of each aspect category.</p>
<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	For example:</p>
<p style="text-align: justify; ">
	“The restaurant was too expensive” → <strong>{price: negative}</strong><br>
	“The restaurant was expensive, but the menu was great” → <strong>{price: negative, food: positive}</strong></p>
<p style="text-align: justify; ">
	&nbsp;</p>
<p style="text-align: justify; ">
	<strong>Datasets</strong>:</p>
<p>
	Two domain-specific datasets for laptops and restaurants, consisting of
 over 6K sentences with fine-grained aspect-level human annotations have
 been provided for training.</p>
<p>
	&nbsp;</p>
<p>
	<em>Restaurant reviews</em>:</p>
<p>
	This dataset consists of over 3K English sentences from the restaurant 
reviews of Ganu et al. (2009). The original dataset of Ganu et al. 
included annotations for coarse aspect categories (Subtask 3) and 
overall sentence polarities; we modified the dataset to include 
annotations for aspect terms occurring in the sentences (Subtask 1), 
aspect term polarities (Subtask 2), and aspect category-specific 
polarities (Subtask 4). We also corrected some errors (e.g., sentence 
splitting errors) of the original dataset. Experienced human annotators 
identified the aspect terms of the sentences and their polarities 
(Subtasks 1 and 2).&nbsp;Additional restaurant reviews, not in the 
original dataset of Ganu et al. (2009), are being annotated in the same 
manner, and they will be used as test data.&nbsp;</p>
<p>
	&nbsp;</p>
<p>
	<em>Laptop reviews</em>:</p>
<p>
	This dataset consists of over 3K English sentences extracted from 
customer reviews of laptops. Experienced human annotators tagged the 
aspect terms of the sentences (Subtask 1) and their polarities (Subtask 
2). This dataset will be used only for Subtasks 1 and 2. Part of this 
dataset will be reserved as test data.&nbsp;</p>
<p>
	&nbsp;</p>
<p>
	<strong>Dataset format:</strong></p>
<p>
	The sentences in the datasets are annotated using XML tags.</p>
<p>
	The following example illustrates the format of the annotated sentences of the restaurants dataset.</p>
<p>
	&nbsp;</p>
<p>
	&lt;sentence id="813"&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;text&gt;All
 the appetizers and salads were fabulous, the steak was mouth watering 
and the pasta was delicious!!!&lt;/text&gt;<br>
	&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerms&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerm
 term="appetizers" polarity="positive" from="8" to="18"/&gt;<br>
	&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 
&lt;aspectTerm term="salads" polarity="positive" from="23" to="29"/&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerm
 term="steak" polarity="positive" from="49" to="54"/&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerm
 term="pasta" polarity="positive" from="82" to="87"/&gt;<br>
	&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;/aspectTerms&gt;<br>
	&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectCategories&gt;<br>
	&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectCategory
 category="food" polarity="positive"/&gt;<br>
	&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;/aspectCategories&gt;<br>
	&lt;/sentence&gt;</p>
<p>
	&nbsp;</p>
<p>
	The possible values of the polarity field are: “positive”, “negative”, 
“conflict”, “neutral”. The possible values of the category field are: 
“food”, “service”, “price”, “ambience”, “anecdotes/miscellaneous”.</p>
<p>
	&nbsp;</p>
<p>
	The following example illustrates the format of the annotated sentences
 of the laptops dataset. The format is the same as in the restaurant 
datasets, with the only exception that there are no annotations for 
aspect categories. Notice that we annotate only aspect terms naming 
particular aspects (e.g., “everything about it” does not name a 
particular aspect).</p>
<p>
	&nbsp;</p>
<p>
	&lt;sentence id="353"&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;text&gt;From
 the build quality to the performance, everything about it has been 
sub-par from what I would have expected from Apple.&lt;/text&gt;<br>
	&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerms&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerm
 term="build quality" polarity="negative" from="9" to="22"/&gt;<br>
	
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;aspectTerm
 term="performance" polarity="negative" from="30" to="41"/&gt;<br>
	&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&lt;/aspectTerms&gt;<br>
	&lt;/sentence&gt;</p>
<p>
	&nbsp;</p>
<p>
	In the sentences of both datasets, there is an &lt;aspectTerm … /&gt; 
element for each occurrence of an aspect term. For example, if the 
previous sentence contained two occurrences of the aspect term 
“performance”, there would be two &lt;aspectTerm … /&gt; elements, which
 would be identical if both occurrences had negative polarity. If a 
sentence has no aspect terms, there is no &lt;aspectTerms&gt; … 
&lt;/aspectTerms&gt; element in its annotations, and similarly for the 
aspect categories in the restaurants dataset.</p>
<p>
	&nbsp;</p>
<p>
	Please, note that:</p>
<ol>
	<li>
		<em>Any quote within an aspect term (e.g., "sales" team) has been replaced with&nbsp;<strong>&amp;quot;&nbsp;</strong>(the text and the offsets remain the same),&nbsp;e.g., &lt;aspectTerm term="&amp;quot;sales&amp;quot; team" .../&gt;.</em></li>
	<li>
		<em>The sentences may contain spelling mistakes. The identified aspect
 terms should be returned as they appear in the sentences, even if 
misspelled (e.g.,&nbsp;</em><em>"warranty" as "warrenty"</em><em>).</em></li>
	<li>
		<em>For each aspect term of the training data we include two attributes ("from and "to") that indicate&nbsp;</em><em>its start and end offset in the text (e.g., &lt;aspectTerm term="staff" polarity="negative" from="8" to="13"/&gt;).</em></li>
</ol>
<p>
	<strong>Evaluation</strong>:</p>
<p>
	Details of the evaluation measures will become available in due time.</p>
<p>
	All participating teams have been provided with annotated training data
 (sentences from the two datasets) in the format discussed above, to 
train their systems. During the SemEval evaluation phase, unlabeled test
 data will be provided. Similar to previous SemEval Sentiment Analysis 
tasks, each team may submit two runs:</p>
<ul>
	<li>
		<strong>Constrained</strong> - using only the provided training data and other resources, such as lexicons</li>
	<li>
		<strong>Unconstrained</strong> - using additional data for training. Teams will be asked to report what resources they used for each submitted run.<br>
		&nbsp;</li>
</ul>
<p>
	<strong>References</strong>:</p>
<p>
	G. Ganu, N. Elhadad, and A. Marian, “Beyond the stars: Improving rating
 predictions using review text content”. Proceedings of the 12th 
International Workshop on the Web and Databases, Providence, Rhode 
Island, 2009.<br>
	M. Hu and B. Liu, “Mining and summarizing customer reviews”. Proceedings of the 10th KDD, pp. 168–177, Seattle, WA, 2004.<br>
	S.-M. Kim and E. Hovy, “Extracting opinions, opinion holders, and 
topics expressed in online news media text”. Proceedings of the Workshop
 on Sentiment and Subjectivity in Text, pp. 1– 8, Sydney, Australia, 
2006.<br>
	B. Liu, Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan &amp; Claypool, 2012.<br>
	S. Moghaddam and M. Ester, “Opinion digger: an unsupervised opinion 
miner from unstructured product reviews”. Proceedings of the 19th CIKM, 
pp. 1825–1828, Toronto, ON, 2010.<br>
	M. Tsytsarau and T. Palpanas. “Survey on mining subjective data on the 
web”. Data Mining and Knowledge Discovery, 24(3):478–514, 2012.<br>
	Z. Zhai, B. Liu, H. Xu, and P. Jia. “Clustering product features for 
opinion mining”.&nbsp; Proceedings of the 4th International Conference 
of WSDM, pp. 347–354, Hong Kong, 2011.<br>
	S. Brody and N. Elhadad. “An unsupervised aspect-sentiment model for 
online reviews”. Proceedings of NAACL, pages 804–812, Los Angeles, CA, 
2010.<br>
	&nbsp;</p>
			</div>
			<div class="cleared"></div>
			<div class="bhm-postfootericons bhm-metadata-icons">
			<span class="bhm-postcategoryicon"></span></div>
			</div><div class="cleared"></div>
			</div></div><div class="cleared"></div></div>
			<div class="bhm-layout-cell bhm-sidebar1">						
			<!------>
				<div class="bhm-box bhm-block">
				<div class="bhm-box-body bhm-block-body">
				<div class="bhm-bar bhm-blockheader">
				<h3 class="t">Contact Info</h3>
				</div>
					<div class="bhm-box bhm-blockcontent">
					<div class="bhm-box-body bhm-blockcontent-body">
					<div>
					<h4>Organizers</h4>
<br>
<ul>
	<li><a href="http://www.aueb.gr/users/ion/">Ion Androutsopoulos</a> (Athens University of Economics and Business, Greece)</li>
	<li><a href="http://www.ilsp.gr/en/profile/staff?view=member&amp;task=show&amp;id=116">Dimitris Galanis</a> (“Athena” Research Center, Greece)</li>
	<li><a href="http://www.cs.york.ac.uk/~suresh">Suresh Manandhar</a> (University of York, UK) <strong>[Primary Contact]</strong></li>
	<li><a href="http://www.ilsp.gr/en/profile/staff?view=member&amp;task=show&amp;id=49">Harris Papageorgiou</a> ("Athena" Research Center, Greece)</li>
	<li><a href="http://nlp.cs.aueb.gr/people.html">John Pavlopoulos</a> (Athens University of Economics and Business, Greece)</li>
	<li><a href="http://www.ilsp.gr/en/profile/staff?view=member&amp;task=show&amp;id=114">Maria Pontiki</a> (“Athena” Research Center, Greece)</li>
</ul>

<br> 
<b>email:</b>
<i>semeval-absa@googlegroups.com </i>
					</div>
					<div class="cleared"></div></div></div>
					<div class="cleared"></div></div></div>
				<div class="bhm-box bhm-block">
				<div class="bhm-box-body bhm-block-body">
				<div class="bhm-bar bhm-blockheader">
				<h3 class="t">Other Info</h3></div>
				<div class="bhm-box bhm-blockcontent">
				<div class="bhm-box-body bhm-blockcontent-body"><div>
				<h4>Announcements</h4>
<br>
<ul>
  <li><a href="http://alt.qcri.org/semeval2014/task4/data/uploads/se2014_absa_submissions.xlsx">Official results</a></li><a href="http://alt.qcri.org/semeval2014/task4/data/uploads/se2014_absa_submissions.xlsx">
  </a><li><a href="http://alt.qcri.org/semeval2014/task4/data/uploads/se2014_absa_submissions.xlsx"></a><a href="http://alt.qcri.org/semeval2014/task4/data/uploads/submission-guidelines.pdf">Submission guidelines</a> are available!</li>
  <li>Baselines, evaluation and validation code are available!</li>
  <li>Annotation guidelines are available</li>
  <li>Train data with minor corrections are now available</li>
  <li>Train data released!</li>
  <li>Join our <a href="https://groups.google.com/forum/#%21forum/semeval-absa">Google group</a>!</li>
  <li>Trial data released!</li>
</ul>				</div><div class="cleared"></div></div></div>
				<div class="cleared"></div></div></div>                
				<div class="cleared"></div>
                                
                                <div class="bhm-box bhm-block">
				<div class="bhm-box-body bhm-block-body">
				<div class="bhm-bar bhm-blockheader">
				<h3 class="t">Last updated on</h3></div>
				<div class="bhm-box bhm-blockcontent">
				<div class="bhm-box-body bhm-blockcontent-body"><div>
				12/5/2014				</div><div class="cleared"></div></div></div>
				<div class="cleared"></div></div></div>                
				<div class="cleared"></div>
			<!----->
		<div class="cleared"></div>
		</div></div></div></div>
		<div class="cleared"></div>
		<div class="bhm-footer">
		<div class="bhm-footer-body">
		<div class="bhm-footer-text">
		Copyright 2018 - <strong>SemEval-2014 Task 4. All Right Reserved
		</strong></div><strong>
		<div class="cleared"></div>
		</strong></div></div><div class="cleared"></div></div></div><strong>
	<div class="cleared"></div>
	<!-- Please don't remove this link -->
	<p class="bhm-page-footer">Designed by <a href="http://www.balinesehandmade.com/">BhMad Studio</a> &nbsp;|&nbsp; <a href="http://get-simple.info/">Powered by  GetSimple</a></p>
	<!-- end -->
	<div class="cleared"></div>
</strong></div></body></html>